@Article{2018.100000058,
AUTHOR = {Mahiye Uluyagmur Ozturk, Ayse Rodopman Arman, Gresa Carkaxhiu Bulut, Onur Tugce Poyraz Findik, Sultan Seval Yilmaz, Herdem Aslan Genc, M. Yanki Yazgan, Umut Teker, Zehra Cataltepe},
TITLE = {Statistical Analysis and Multimodal Classification on Noisy Eye Tracker and  Application Log Data of Children with Autism and ADHD},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {24},
YEAR = {2018},
NUMBER = {4},
PAGES = {891--905},
URL = {http://www.techscience.com/iasc/v24n4/39813},
ISSN = {2326-005X},
ABSTRACT = {Emotion recognition behavior and performance may vary between people with major 
neurodevelopmental disorders such as Autism Spectrum Disorder (ASD), Attention Deficit 
Hyperactivity Disorder (ADHD) and control groups. It is crucial to identify these differences 
for early diagnosis and individual treatment purposes. This study represents a 
methodology by using statistical data analysis and machine learning to provide help to 
psychiatrists and therapists on the diagnosis and individualized treatment of participants 
with ASD and ADHD. In this paper we propose an emotion recognition experiment 
environment and collect eye tracker fixation data together with the application log data 
(APL). In order to detect the diagnosis of the participant we used classification algorithms 
with the Tomek links noise removing method. The highest classification accuracy results 
were reported as 86.36% for ASD vs. Control, 81.82% for ADHD vs. Control and 70.83% 
for ASD vs. ADHD. This study provides evidence that fixation and APL data have 
distinguishing features for the diagnosis of ASD and ADHD.},
DOI = {10.31209/2018.100000058}
}